Fabricating semiconductor devices such as logic and memory devices typically includes processing a substrate such as a semiconductor wafer using a large number of semiconductor fabrication processes to form various features and multiple levels of the semiconductor devices. As semiconductor device size become smaller and smaller, it becomes critical to develop enhanced inspection and review devices and procedures. One such procedure includes classification and analysis of defects on a specimen, such as a wafer. As used throughout the present disclosure, the term “wafer” generally refers to substrates formed of a semiconductor or non-semiconductor material. For example, a semiconductor or non-semiconductor material may include, but are not limited to, monocrystalline silicon, gallium arsenide, and indium phosphide.
Defect review is a process by which a review tool reviews defects acquired by an inspector or inspection tool. Defect review requires the classification of defects and the differentiation, or separation of defect types based on a set of calculated defect attributes. However, current defect classification approaches have a number of limitations.
First, decision trees for defect classification are often manually created using calculated attributes, which is a time consuming process. In this case, a user has to select the best attributes for each node of a decision tree from a large number (e.g., greater than 80) of attributes. In addition, the tree size may become large (e.g., greater than 50 nodes). In addition, the quality of a manually created tree is related to a user's interpretation and understanding of the available attributes and the decision tree creation process. Further, current approaches to measure defect type separability are limited. Prior approaches require a user to find the best attributes for type pairs manually in order to separate two defect types. Moreover, current approaches to classifier monitoring through the production is time consuming and complex.
As such, it would be advantageous to provide a system and method that provides improved defect classification, defect type separability and classifier monitoring that cures the defects identified above.